In multicausal abductive tasks a person must explain some findings by assembling a composite hypothesis that consists of one or more elementary hypotheses. If there are n elementary hypotheses, there can be up to 2n composite hypotheses. To constrain the search for hypotheses to explain a new observation, people sometimes use their current explanation—the previous evidence and their present composite hypothesis of that evidence; however, it is unclear when and how the current explanation is used. In addition, although a person's (...) current explanation can narrow the search for a hypothesis, it can also blind the problem solver to alternative, possibly better, explanations. This paper describes a model of multicausal abductive reasoning that makes two predictions regarding the use of the current explanation. The first prediction is that the current explanation is not used to explain new evidence if there is a simple (i.e., nondisjunctive, concrete) hypothesis to account for that evidence. The second prediction is that the current explanation is used when attempting to discriminate among several alternative hypotheses for new evidence. These hypotheses were tested in three experiments. The results are consistent with the second prediction: the current explanation is used when discriminating among alternative hypotheses. However, the first prediction—that the current explanation is not used when a simple hypothesis can account for new data—received only limited support. Participants used the current explanation to constrain their interpretation of new data in 46.5% of all trials. This suggests that context‐independent strategies compete with context‐dependent ones—an interpretation that is consistent with recent work on strategy selection during problem solving. (shrink)

One of the most debated questions in psychology and cognitive science is the nature and the functioning of the mental processes involved in deductive reasoning. However, all existing theories refer to a specific deductive domain, like syllogistic, propositional or relational reasoning.Our goal is to unify the main types of deductive reasoning into a single set of basic procedures. In particular, we bring together the microtheories developed from a mental models perspective in a single theory, for which we provide a formal (...) foundation. We validate the theory through a computational model (UNICORE) which allows fine‐grained predictions of subjects' performance in different reasoning domains.The performance of the model is tested against the performance of experimental subjects—as reported in the relevant literature—in the three areas of syllogistic, relational and propositional reasoning. The computational model proves to be a satisfactory artificial subject, reproducing both correct and erroneous performance of the human subjects. Moreover, we introduce a developmental trend in the program, in order to simulate the performance of subjects of different ages, ranging from children (3–6) to adolescents (8–12) to adults (>21). The simulation model performs similarly to the subjects of different ages.Our conclusion is that the validity of the mental model approach is confirmed for the deductive reasoning domain, and that it is possible to devise a unique mechanism able to deal with the specific subareas. The proposed computational model (UNICORE) represents such a unifying structure. (shrink)

In addition to active wake, emotions are generated and experienced in a variety of functionally different states such as those of sleep, during which external stimulation and cognitive control are lacking. The neural basis of emotions can be specified by regarding the multitude of emotion-related brain states, as well as the distinct neuro- and psychodynamic stages (generation and regulation) of emotional experience.

Imperative sentences like Dance! do not seem to represent the world. Recent modal analyses challenge this idea, but its intuitive and historical appeal remain strong. This paper presents three new challenges for a non-representational analysis, showing that the obstacles facing it are even steeper than previously appreciated. I will argue that the only way for the non-representationalist to meet these three challenges is to adopt a dynamic semantics. Such a dynamic semantics is proposed here: imperatives introduce preferences between alternatives. This (...) characterization of meaning focuses on what function a sentence serves in discourse, rather than what that sentence refers to (e.g., a state of the world). By representing the meaning of imperatives, connectives and declaratives in a common dynamic format, the challenges posed for non-representationalism are met. (shrink)

The mind-society problem deals with the relations between mental and social phenomena. The problem is crucial in the main methodologies of social sciences. The thesis of hermeneutics is that we can only understand but not explain the relationship between beliefs and social action because mental and social events are not natural events. The thesis of social holism is that social phenomena are emergent and irreducible to mental phenomena. The thesis of rational choice theory is that social phenomena are reducible to (...) mental phenomena and this reduction is explained by unrealistic a priori principles of rationality. These theses depend on their different solutions to the following fundamental philosophical issues: the mind-body identity; the causal nature of social explanation; the realistic goal of science. A positive answer to these issues implies support of a different solution to the mind-society problem: social phenomena are conventional concepts and they can be reduced and explained in terms of realistic concepts describing the causal mechanisms of individual reasoning and decision-making. Cognitive psychology seems to supply us some initial tentative models to explain social action, but research into neuropsychology might be able to generate the proper causal representations of the relation between mind and action. (shrink)

This commentary gives a personal perspective on modeling and modeling developments in cognitive science, starting in the 1950s, but focusing on the author’s personal views of modeling since training in the late 1960s, and particularly focusing on advances since the official founding of the Cognitive Science Society. The range and variety of modeling approaches in use today are remarkable, and for many, bewildering. Yet to come to anything approaching adequate insights into the infinitely complex fields of mind, brain, and intelligent (...) systems, an extremely wide array of modeling approaches is vital and necessary. (shrink)

We relate the theory of presupposition accommodation to a computational framework for reasoning in conversation. We understand presuppositions as private commitments the speaker makes in using an utterance but expects the listener to recognize based on mutual information. On this understanding, the conversation can move forward not just through the positive effects of interlocutors’ utterances but also from the retrospective insight interlocutors gain about one anothers’ mental states from observing what they do. Our title, ENLIGHTENED UPDATE, highlights such cases. Our (...) approach fleshes out two key principles: that interpretation is a form of intention recognition; and that intentions are complex informational structures, which specify commitments to conditions and to outcomes as well as to actions. We present a formalization and implementation of these principles for a simple conversational agent, and draw on this case study to argue that pragmatic reasoning is holistic in character, continuous with common-sense reasoning about collaborative activities, and most effectively characterized by associating specific, reliable interpretive constraints directly with grammatical forms. In showing how to make such claims precise and to develop theories that respect them, we illustrate the general place of computation in the cognitive science of language. (shrink)

We see no grounds for insisting that, because the concept natural number is abstract, its foundations must be innate. It is possible to specify domain general learning processes that feed into more abstract concepts of numerical infinity. By neglecting the messiness of children's slow acquisition of arithmetical concepts, Rips et al. present an idealized, unnecessarily insular, view of number development.

In this paper I analyse different justifications for the claim that the minor premise of the libertarian argument is true, namely, intuition, van Inwagen’s argument from moral responsibility and an argument from rationality. I claim none of these is satisfactory. I conclude by suggesting a possible way of interpreting the meaning of the free will intuition libertarians claim we have.

This paper focuses on the relation of heuristic search and level of intelligence in cognitive computation systems. The paper begins with a review of the fundamental properties of a cognitive computation system, which is defined generally as a control system that generates goal-directed actions in response to environmental inputs and constraints. An important property of cognitive computations is the need to process local cues in symbol structures to access and integrate distal knowledge to generate a response. To deal with uncertainties (...) involved in this local-to-distal processing, the system needs to perform heuristic search to locate and integrate the right set of distal structures. The level of intelligence of the system depends critically on the efficiency of the heuristic search process. It is argued that, for a bounded rationality system, the level of intelligence does not depend on how much search it needs to do to accomplish a task. Rather, the level of intelligence depends on how much search it does not need to do to achieve the same level of performance. Examples were discussed to illustrate this idea. The first two examples show how machines that play games like tic-tac-toe and chess rely heavily on the efficiency of the heuristic search algorithm to achieve better performance, demonstrating the relation of heuristic search and intelligence in a bounded rationality system. The second example shows how humans adapt to different information ecologies to perform information search on the Internet and how their performance improves over time, demonstrating how heuristic search can be improved in an adaptive rationality system. The two examples demonstrate how better search control knowledge and representations of task environment can improve the efficiency of heuristic search, thereby improving the intelligence of the system. (shrink)

We present a comprehension‐based computational model of UNIX user skill acquisition and performance in a training context (UNICOM). The work extends a comprehension‐based theory of planning to account for skill acquisition and learning. Individual models of 22 UNIX users were constructed and used to simulate user performance on successive command production problems in a training context. Comparisons of model and the human empirical data result in a high degree of agreement, validating the ability of UNICOM to predict user response to (...) training. (shrink)

By the early part of the twentieth century, academia in the English-speaking world had stabilized (or ossified!) into a set of scientific and humanistic disciplines that still survives at the century’s end. The natural sciences have such disciplines as physics, chemistry, and biology, and the social sciences include economics, psychology, and sociology. These disciplines provide a convenient organizing principle for university departments and professional organizations, but they often bear little relation to cuttingedge research, which can concern topics that cut across (...) or occur at the boundaries of two or more of the established disciplines. When this happens, productive research and teaching must be interdisciplinary. Cognitive science is the interdisciplinary study of mind, embracing psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. It is undoubtedly one of the major interdisciplinary successes of the twentieth century, with its own society, journal, and textbooks, and with more than sixty cognitive science programs established at universities in North American and Europe. This paper is an attempt to answer the question: What are the factors contributing to the success of the interdisciplinary field of cognitive science? My discussion is organized around the metaphor of the trading zone, a novel and fertile analogy that Gallison (1997) developed for his rich and detailed discussion of the practices of twentieth-century physics. To understand the diverse groups of December 1, 2006 experimenters and theoreticians, Gallison presents their interactions in terms of the trading zones described by anthropologists: Subcultures trade. Anthropologists have extensively studied how different groups, with radically different ways of dividing up the world and symbolically organizing its parts, can not only exchange goods but also depend essentially on those trades.. (shrink)

Many debates about consciousness appear to be endless, in part because of conceptual confusions preventing clarity as to what the issues are and what does or does not count as evidence. This makes it hard to decide what should go into a machine if it is to be described as 'conscious'. Thus, triumphant demonstrations by some AI developers may be regarded by others as proving nothing of interest because the system does not satisfy *their* definitions or requirements specifications.